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Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise
Andrew Zammit-Mangion1, Jonathan Rougier2, Nana Schön3
1School of Geographical Sciences, University of Bristol Bristol, BS8 1SS, U.K. ; Department of Mathematics, University of Bristol Bristol, BS8 1TW, U.K.
Environmetrics
|May 5, 2015
Summary
Antarctica
Area of Science:
- * Glaciology
- * Climate Science
- * Environmental Statistics
Background:
- * Antarctica holds vast freshwater resources, with ice sheet melt significantly impacting global sea levels.
- * Accurate estimation of ice sheet contributions to sea-level rise (SLR) is crucial for coastal planning and climate projections.
- * Integrating diverse remote sensing and in situ data is essential for a comprehensive understanding of ice sheet mass balance.
Purpose of the Study:
- * To develop a statistical framework for estimating Antarctica's contribution to present-day sea-level rise.
- * To disentangle the geophysical processes driving ice sheet height changes and mass exchange with the ocean.
- * To assess the feasibility and insights gained from estimating SLR contributions without relying solely on numerical models.
Main Methods:
- * Application of a multivariate spatio-temporal model, approximated as a Gaussian Markov random field.
- * Integration of various remote sensing and in situ datasets (e.g., GPS) with differing characteristics.
- * Parameter estimation using geophysical models and a Markov chain Monte Carlo (MCMC) scheme in a high-performance computing environment.
Main Results:
- * Predicted rates of height change across Antarctica due to key geophysical processes.
- * Provided estimates of Antarctica's contribution to sea-level rise, including associated uncertainties.
- * Demonstrated the capability to assess SLR contributions independently of explicit numerical ice sheet models.
Conclusions:
- * It is possible and insightful to assess Antarctica's sea-level rise contribution without direct reliance on numerical models.
- * The developed statistical approach offers a valuable method for validating geophysical numerical models, especially where in situ data are scarce.
- * The findings enhance our understanding of ice sheet dynamics and their impact on global sea levels.
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